Design and scale end-to-end data pipelines and RAG architectures for LLM consumption: ETL/ELT, vector databases, chunking/embeddings, data cleaning/PII removal, metadata engineering, low-latency streaming, and evaluation/versioning to support agentic AI.
We are seeking a Lead Data Engineer to build and scale the data infrastructure powering our Agentic AI products. You will be responsible for the "Ingestion-to-Insight" pipeline that allows autonomous agents to access, search, and reason over vast amounts of proprietary and public data.
Your role is critical: you will design the RAG (Retrieval-Augmented Generation) architectures and data pipelines that ensure our agents have the right context at the right time to make accurate decisions.
Key Responsibilities
- AI-Ready Data Pipelines: Design and implement scalable ETL/ELT pipelines that process both structured (SQL, logs) and unstructured (PDFs, emails, docs) data specifically for LLM consumption.
- Vector Database Management: Architect and optimize Vector Databases (e.g., Pinecone, Weaviate, Milvus, or Qdrant) to ensure high-speed, relevant similarity searches for agentic retrieval.
- Chunking & Embedding Strategies: Collaborate with AI Engineers to optimize data chunking strategies and embedding models to improve the "recall" and "precision" of the agent's knowledge retrieval.
- Data Quality for AI: Develop automated "Data Cleaning" workflows to remove noise, PII (Personally Identifiable Information), and toxicity from training/context datasets.
- Metadata Engineering: Enrich raw data with advanced metadata tagging to help agents filter and prioritize information during multi-step reasoning tasks.
- Real-time Data Streaming: Build low-latency data streams (using Kafka or Flink) to provide agents with "fresh" data, enabling them to act on real-time market or operational changes.
- Evaluation Frameworks: Construct "Gold Datasets" and versioned data snapshots to help the team benchmark agent performance over time.
Required Skills & Qualifications
- Experience: 10+ years in Data Engineering, with at least 2 years focusing on data for LLMs or AI/ML applications.
- Python Mastery: Deep expertise in Python (Pandas, Pydantic, FastAPI) for data manipulation and API integration.
- Data Tooling: Strong experience with modern data stack tools (e.g., dbt, Airflow, Dagster, Snowflake, or Databricks).
- Vector Expertise: Hands-on experience with at least one major Vector Database and knowledge of similarity search algorithms (HNSW, Cosine Similarity).
- Search Knowledge: Familiarity with hybrid search techniques (combining semantic search with traditional keyword search like Elasticsearch/BM25).
- Cloud Infrastructure: Proficiency in managing data workloads on AWS, Azure, or GCP.
Preferred Qualifications
- Experience with LlamaIndex or LangChain for data ingestion.
- Knowledge of Graph Databases (e.g., Neo4j) to help agents understand complex relationships between data points.
- Familiarity with "Data-Centric AI" principles—prioritizing data quality over model size.
Compensation, Benefits and Duration
Minimum Compensation: USD 46,000
Maximum Compensation: USD 162,000
Compensation is based on actual experience and qualifications of the candidate. The above is a reasonable and a good faith estimate for the role.
Medical, vision, and dental benefits, 401k retirement plan, variable pay/incentives, paid time off, and paid holidays are available for full time employees.
This position is not available for independent contractors
No applications will be considered if received more than 120 days after the date of this post
Similar Jobs
Cloud • Information Technology • Security • Software • Cybersecurity
Leads strategic relationships with private equity firms, developing joint go-to-market plans, originating portfolio company opportunities, structuring commercial deals, and driving adoption of Zscaler’s security platform. The role partners with PE leaders, sales teams, business value specialists, and technical experts to quantify value, improve EBITDA, mitigate cyber risk, support M&A transactions, and advance digital transformation across portfolio companies.
Top Skills:
Ai/MlCybersecurityDigital TransformationEbitdaIrrNetwork SecurityQbrsRoiSales ForecastingSaseTcoZero Trust Exchange
Artificial Intelligence • Cloud • HR Tech • Information Technology • Productivity • Software • Automation
Drive net-new business growth within a strategic enterprise technology account. Identify expansion opportunities, build business cases and solution roadmaps, develop relationships from IT teams through C-suite executives, coordinate cross-functional sales and partner teams, and close new solution areas while achieving license, renewal, professional services, and annual sales targets.
Top Skills:
Artificial IntelligenceCustomer Service ManagementHr Service DeliveryIt Operations ManagementIt Service ManagementSecurity OperationsServicenow
Artificial Intelligence • Fintech • Insurance • Marketing Tech • Software • Analytics
Handles inbound calls and warm leads to understand customers’ insurance needs, recommend appropriate Property & Casualty coverage, and convert prospects into policyholders. The role includes paid licensing and training, customer consultation, sales closing, brand representation, and maintaining a reliable remote work environment. Employees must work four weekdays and one weekend day, meet home-office and internet requirements, and remain in their resident state for at least one year.
Top Skills:
PcWired High-Speed Internet
What you need to know about the Boston Tech Scene
Boston is a powerhouse for technology innovation thanks to world-class research universities like MIT and Harvard and a robust pipeline of venture capital investment. Host to the first telephone call and one of the first general-purpose computers ever put into use, Boston is now a hub for biotechnology, robotics and artificial intelligence — though it’s also home to several B2B software giants. So it’s no surprise that the city consistently ranks among the greatest startup ecosystems in the world.
Key Facts About Boston Tech
- Number of Tech Workers: 269,000; 9.4% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Thermo Fisher Scientific, Toast, Klaviyo, HubSpot, DraftKings
- Key Industries: Artificial intelligence, biotechnology, robotics, software, aerospace
- Funding Landscape: $15.7 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Summit Partners, Volition Capital, Bain Capital Ventures, MassVentures, Highland Capital Partners
- Research Centers and Universities: MIT, Harvard University, Boston College, Tufts University, Boston University, Northeastern University, Smithsonian Astrophysical Observatory, National Bureau of Economic Research, Broad Institute, Lowell Center for Space Science & Technology, National Emerging Infectious Diseases Laboratories
